Four Step Model

For this analysis we are using a classic four-step travel demand model. Because this is a student assignment with limited capacity, we are using various shortcuts throughout the model that will be identified. Shortcomings with the way we have modeled transportation do not necessarily reflect shortcomings of the four-step model.

Figure 3: Overview of the four-step model

Figure 4: Number of households by census tract

Transit Analysis Zones

Our transportation analysis looks at 419 transit analysis zones across 7 counties, each corresponding to a census tract. In Figure 4 we can see that Oklahoma City follows a typical greater metropolitan area pattern with a dense and active urban core, surrounded by suburbs and rural areas. The

Road Network

The longest distance between zones by car was just over 3 hours and 15 minutes (190.5 minutes). The shortest distance was half a minute (0.5 minutes). The average distance between TAZ centroids is roughly 30 minutes (30.7), the median time is around 25 minutes (25.6 minutes). Roads highlighted in red in Figure 5 were modeled as two-way rural roads.

Figure 5: The modeled Oklahoma City road network.

Public Transit Network

Figure 6: Full county map with public transit.

The OKC Transit network is composed of 651 miles of bus routes, across 30 different bus lines. The map below shows the bus network in detail, and in the context of the whole city. Of our 419 transit analysis zones for OKC metro area, the transit network connects only 135 of those zones, with the longest travel time between zones being just over 3 hours and 15 minutes (190.5 minutes). The shortest distance was half a minute (0.5 minutes). The average distance between centroids is roughly 30 minutes (30.7). The median time between centroids was around 25 minutes (25.6 minutes). The public transit is fully contained in 3 of the 7 counties that make up the OKC statistical area.

Figure 7: The public transit network in Oklahoma City.

Travel Times

Using the networks we created, we generated travel time skims which provide travel times between every TAZ. By selecting a subset we can map every zone’s travel time by car to the University of Oklahoma, as in Figure 5.

Figure 8: Travel time by car from the University of Oklahoma

As well as the travel time by bus from the University to other zones, as in Figure 6 (note that many are grayed out because they cannot be reached by bus).

Figure 9: Public transit travel time to the University of Oklahoma

Accessibility by Car

Car access is distributed as is typical for a metropolitan area: the downtown, being both dense and centrally located, has higher scores than the outlying areas.

Figure 10: Car accessibility scores for each zone

While they don’t necessarily represent a large percent of the land area, there are many of those downtown high-scoring zones because they are smaller, and this is what forms the right peak in the distribution shown in Figure 11. The left peak represents the outlying rural zones.

Figure 11: Distribution of zone accessibility scores

Accessibility by Transit

Transit in Oklahoma City is quite limited to the areas in and around Downtown and the University campus. The bus lines between the two areas notably bypass most of the zones in between, creating the two island-like regions in Figure 12. Taking a look at the linear scale accessibility map, we can see that the majority of those zones have very similar low scores. There are just a few outliers with much higher accessibility scores due to the proximity of transit hubs where many of the bus lines meet.

Figure 12: Transit accessibility scores for each zone (on a log scale and a linear scale)

Those outliers can also be seen in Figure 13 at the far right tail of the distribution.

Figure 13: Distribution of zone accessibility scores

Estimating Productions and Attractions

To generate Trip Attractions and Trip Productions for each transit analysis zones, we broke up trip types into three main categories.

1. Home Based Work: Travel between work and home
2. Home Based Other: Travel between home and places other than a workplace
3. Non-Home Based: Travel that does not start or end at home

To generate our trip attractions and productions we conducted a linear regression using factors present in both the NHTS22 National Household Travel Survey (NHTS) from 2017 and the ACS.

We used:

1. Median Income (Continuous)
2. Whether or not the household had a vehicle (yes, no)
3. Household size (1 person, 2 people, 3 people, 4 or more people)
4. Whether or not the household had kids (yes, no)

We had a very low R-Squared value in our regressions ranging from .124 to .129. In our regressions, only household size and presence of kids was statistically significant. The resulting trips by type were as follows:

Figure 14: Home Based Other, Trip Productions and Attractions

## Reading layer `trip-gen-newVariables_scenario' from data source 
##   `C:\Users\nursh\OneDrive - Harvard University\Desktop\Travel Behavior and Forecasting\TravelBehaviorForecasting\data\trip-gen-newVariables_scenario.geojson' 
##   using driver `GeoJSON'
## Simple feature collection with 409 features and 8 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -98.3 ymin: 34.7 xmax: -96.6 ymax: 36.2
## Geodetic CRS:  NAD83

Figure 15: Home Based Other in Scenario, Trip Productions and Attractions

Figure 16: Non-Home Based, Trip Productions and Attractions

We also used NHTS data to examine mode share in OKC based on various trip types.

Figure 17: Mode share in the OKC metro area

Figure 18: Trip purpose by mode share in Oklahoma City metro area.

Figure 19: Detailed trip purpose and mode share in Oklahoma City Metro Area

Calibrating the Model with Friction Functions

To build our gravity model and calculate our trip attraction/production matrix, we first had to choose friction functions for all three trip types. We chose to use power functions, with coefficients adjusted to cause our model’s average travel times to match the NHTS travel data.

For home-based work trips, \(friction = travelTime^{-3}\)

For home-based other trips, \(friction = travelTime^{-0.5}\)

For non-home-based trips, \(friction = travelTime^{-2.9}\)

This produced average travel times that closely matched observed data, as shown below.

Illustrating Travel Flows

We can see that the single county which generates the most trips by far (across all three types) originate in Cleveland county. This county includes the University of Oklahoma and a large amount of residential land. The vast majority of those trips end in Oklahoma County, which contains the Downtown area. Both the desire line plot and Chord diagrams below illustrate this.

Figure 20: Desire lines plotted between counties in Oklahoma City.

Generating Travel Costs

For this step of the model we began by generating costs for our three forms of transportation: driving alone (SOV), driving with someone else (HOV), and taking transit. Although our transit skim contained fare information, we chose to instead use information from the National Transit Database as we thought it would better reflect information about transit discounts. We then made the assumption that transit cost is a function of the baseline fare cost multiplied by the number of transfers. For travel by car we used NHTS data to first find total expenditure on gas and total driving time. Using these two numbers we generated cost per minute of driving. We then used information from table 4.16 of NCHRP 716 to estimate average occupancy of vehicles for our different categories of trip types (Home Based Work, Non-Home Based, Home-Based Other). We assume that driving cost is shared equally among all car occupants.

Estimating Mode Shares

We then went into NHTS data to pull survey data about the existing mode share in OKC. We divided the modes into our three categories: HOV, SOV, and Transit.

We made the following assumptions:

We then used the NHTS survey weights to determine the percent of each of these three modes over our study area.

Choosing Models

In our process of choosing mode choice models from NCHRP 716 we went with two criteria 1. Using a Nested Logit when possible 2. Using models that we had all values for

We chose the following models:

Calculating Mode Share by Applying a Mode-Choice Model

Setting up the Scenario

Area of Interest

This scenario is concerned with the area of Oklahoma City contained within the Land Run of 1895. We geolocated the map in Figure 25 to identify which census tracts would become part of the new urban center. In total, six tracts with a total area of 353 square miles would be included.

Figure 25: A map of the Land Run of 1895

Figure 26: The census tracts that would be returned to Kickapoo nation sovereignty

Results

Flows

Figure 27: Desire lines plotted between counties in Oklahoma City.

Figure 28: Increases in travel flow between counties in Oklahoma City.

Figure 29: Decreases in travel flow between counties in Oklahoma City

Figure 30: Chord diagram for home-based work trips.

Figure 31: Chord diagram for home-based other trips.

Figure 32: Chord diagram for non-home-based trips.

Vehicle Miles Traveled and Congestion

Old VMT: 22006966

New VMT: 40856275

Distribution of congestion shows more for our land back scenario

Figure 33: Histogram of road segment congestion in new scenario vs current conditions

A: Methodology and Sources

B: Assumptions

C: Supplemental Visualizations

Census and Employment Data

Figure 34: Employment is concentrated in the downtown area. Employment information is not available for many of our Transit Analysis Zones

Figure 35: Majority of employment in OKC metro area is in the service industry.

Fig A2: Majority of employment in OKC metro area is in the service industry

Fig A3: Employment + Activity Density is greatest in downtown OKC

Fig A4: The majoirty of census tracts have fewer than 100 households with no cars. But some tracts have more than 500 households with no vehicles.

Figure 36: Vehicle Ownership Dot Density Map

Figure 37: Highest income neighborhoods are north of downtown.

Figure 38: Census tracts by income, population density, and # of adults Living with their parents.